Project portfolio selection through decision support
Decision Support Systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Solving the sorting network problem using iterative optimization with evolved hypermutations
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective prototype optimization with evolved improvement steps
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Software Cost Estimation with COCOMO II
Software Cost Estimation with COCOMO II
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective simulation optimisation in software project management
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Cooperative co-evolutionary optimization of software project staff assignments and job scheduling
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Empirical findings on team size and productivity in software development
Journal of Systems and Software
Dynamic index tracking via multi-objective evolutionary algorithm
Applied Soft Computing
| Hi-index | 0.00 |
Large software companies have to plan their project portfolio to maximize potential portfolio return and strategic alignment, while balancing various preferences, and considering limited resources. Project portfolio managers need methods and tools to find a good solution for complex project portfolios and multiobjective target criteria efficiently. However, software project portfolios are challenging to describe for optimization in a practical way that allows efficient optimization. In this paper we propose an approach to describe software project portfolios with a set of multiobjective criteria for portfolio managers using the COCOMO II model and introduce a multiobjective evolutionary approach, mPOEMS, to find the Pareto-optimal front efficiently. We evaluate the new approach with portfolios choosing from a set of 50 projects that follow the validated COCOMO II model criteria and compare the performance of the mPOEMS approach with state-of-the-art multiobjective optimization evolutionary approaches. Major results are as follows: the portfolio management approach was found usable and useful; the mPOEMS approach outperformed the other approaches.